def py2rpy_pandasseries(obj):
if obj.dtype.name == 'O':
warnings.warn('Element "%s" is of dtype "O" and converted '
'to R vector of strings.' % obj.name)
res = StrVector(obj)
elif obj.dtype.name == 'category':
res = py2rpy_categoryseries(obj)
res = FactorVector(res)
elif is_datetime64_any_dtype(obj.dtype):
# time series
tzname = obj.dt.tz.zone if obj.dt.tz else ''
d = [
IntVector([x.year for x in obj]),
IntVector([x.month for x in obj]),
IntVector([x.day for x in obj]),
IntVector([x.hour for x in obj]),
IntVector([x.minute for x in obj]),
FloatSexpVector([x.second + x.microsecond * 1e-6 for x in obj])
]
res = ISOdatetime(*d, tz=StrSexpVector([tzname]))
# TODO: can the POSIXct be created from the POSIXct constructor ?
# (is '<M8[ns]' mapping to Python datetime.datetime ?)
res = POSIXct(res)
elif (obj.dtype == dt_O_type):
homogeneous_type = None
for x in obj.values:
if x is None:
continue
if homogeneous_type is None:
homogeneous_type = type(x)
continue
if type(x) is not homogeneous_type:
raise ValueError('Series can only be of one type, or None.')
# TODO: Could this be merged with obj.type.name == 'O' case above ?
res = {
int: IntVector,
bool: BoolVector,
None: BoolVector,
str: StrVector,
bytes: numpy2ri.converter.py2rpy.registry[numpy.ndarray]
}[homogeneous_type](obj)
else:
# converted as a numpy array
func = numpy2ri.converter.py2rpy.registry[numpy.ndarray]
# current conversion as performed by numpy
res = func(obj)
if len(obj.shape) == 1:
if (obj.dtype != dt_O_type):
# force into an R vector
res = as_vector(res)
# "index" is equivalent to "names" in R
if obj.ndim == 1:
res.do_slot_assign('names',
StrVector(tuple(str(x) for x in obj.index)))
else:
res.do_slot_assign('dimnames',
SexpVector(conversion.py2rpy(obj.index)))
return res
def py2ri_pandasseries(obj):
if obj.dtype.name == 'category':
res = py2ri_categoryseries(obj)
res = FactorVector(res)
elif obj.dtype == dt_datetime64ns_type:
# time series
d = [
IntVector([x.year for x in obj]),
IntVector([x.month for x in obj]),
IntVector([x.day for x in obj]),
IntVector([x.hour for x in obj]),
IntVector([x.minute for x in obj]),
IntVector([x.second for x in obj])
]
res = ISOdatetime(*d)
#FIXME: can the POSIXct be created from the POSIXct constructor ?
# (is '<M8[ns]' mapping to Python datetime.datetime ?)
res = POSIXct(res)
else:
# converted as a numpy array
func = numpy2ri.converter.py2ri.registry[numpy.ndarray]
# current conversion as performed by numpy
res = func(obj)
if len(obj.shape) == 1:
if (obj.dtype != dt_O_type):
# force into an R vector
res = as_vector(res)
# "index" is equivalent to "names" in R
if obj.ndim == 1:
res.do_slot_assign('names',
StrVector(tuple(str(x) for x in obj.index)))
else:
res.do_slot_assign('dimnames', SexpVector(conversion.py2ri(obj.index)))
return res
def py2ri_pandasseries(obj):
if obj.dtype == '<M8[ns]':
# time series
d = [
IntVector([x.year for x in obj]),
IntVector([x.month for x in obj]),
IntVector([x.day for x in obj]),
IntVector([x.hour for x in obj]),
IntVector([x.minute for x in obj]),
IntVector([x.second for x in obj])
]
res = ISOdatetime(*d)
#FIXME: can the POSIXct be created from the POSIXct constructor ?
# (is '<M8[ns]' mapping to Python datetime.datetime ?)
res = POSIXct(res)
else:
# converted as a numpy array
res = numpy2ri.numpy2ri(obj.values)
# "index" is equivalent to "names" in R
if obj.ndim == 1:
res.do_slot_assign('names',
StrVector(tuple(str(x) for x in obj.index)))
else:
res.do_slot_assign('dimnames', SexpVector(conversion.py2ri(obj.index)))
return res
def pandas2ri(obj):
if isinstance(obj, PandasDataFrame):
od = OrderedDict()
for name, values in obj.iteritems():
if values.dtype.kind == 'O':
od[name] = StrVector(values)
else:
od[name] = pandas2ri(values)
return DataFrame(od)
elif isinstance(obj, PandasIndex):
if obj.dtype.kind == 'O':
return StrVector(obj)
else:
# only other alternative to 'O' is integer, I think,
# which goes straight to the numpy converter.
return numpy2ri.numpy2ri(obj)
elif isinstance(obj, PandasSeries):
if obj.dtype == '<M8[ns]':
# time series
d = [
IntVector([x.year for x in obj]),
IntVector([x.month for x in obj]),
IntVector([x.day for x in obj]),
IntVector([x.hour for x in obj]),
IntVector([x.minute for x in obj]),
IntVector([x.second for x in obj])
]
res = ISOdatetime(*d)
#FIXME: can the POSIXct be created from the POSIXct constructor ?
# (is '<M8[ns]' mapping to Python datetime.datetime ?)
res = POSIXct(res)
else:
# converted as a numpy array
res = numpy2ri.numpy2ri(obj.values)
# "index" is equivalent to "names" in R
if obj.ndim == 1:
res.do_slot_assign('names', ListVector({'x':
pandas2ri(obj.index)}))
else:
res.do_slot_assign('dimnames', ListVector(pandas2ri(obj.index)))
return res
else:
return original_py2ri(obj)
def py2rpy_pandasseries(obj):
if numpy.dtype.name == 'O':
warnings.warn('Element "%s" is of dtype "O" and converted to R vector of strings.' % obj.name)
res = StrVector(obj)
elif obj.dtype.name == 'category':
res = py2rpy_categoryseries(obj)
res = FactorVector(res)
elif is_datetime64_any_dtype(obj.dtype):
# time series
tzname = obj.dt.tz.zone if obj.dt.tz else ''
d = [IntVector([x.year for x in obj]),
IntVector([x.month for x in obj]),
IntVector([x.day for x in obj]),
IntVector([x.hour for x in obj]),
IntVector([x.minute for x in obj]),
IntVector([x.second for x in obj])]
res = ISOdatetime(*d, tz=StrSexpVector([tzname]))
#FIXME: can the POSIXct be created from the POSIXct constructor ?
# (is '<M8[ns]' mapping to Python datetime.datetime ?)
res = POSIXct(res)
else:
# converted as a numpy array
func = numpy2ri.converter.py2rpy.registry[numpy.ndarray]
# current conversion as performed by numpy
res = func(obj)
if len(obj.shape) == 1:
if (obj.dtype != dt_O_type):
# force into an R vector
res=as_vector(res)
# "index" is equivalent to "names" in R
if obj.ndim == 1:
res.do_slot_assign('names',
StrVector(tuple(str(x) for x in obj.index)))
else:
res.do_slot_assign('dimnames',
SexpVector(conversion.py2rpy(obj.index)))
return res
def rpy2py_floatvector(obj):
if POSIXct.isrinstance(obj):
return rpy2py(POSIXct(obj))
else:
return numpy2ri.rpy2py(obj)
def py2rpy_pandasseries(obj):
if obj.dtype.name == 'O':
warnings.warn('Element "%s" is of dtype "O" and converted '
'to R vector of strings.' % obj.name)
res = StrVector(obj)
elif obj.dtype.name == 'category':
res = py2rpy_categoryseries(obj)
res = FactorVector(res)
elif is_datetime64_any_dtype(obj.dtype):
# time series
tzname = obj.dt.tz.zone if obj.dt.tz else ''
d = [IntVector([x.year for x in obj]),
IntVector([x.month for x in obj]),
IntVector([x.day for x in obj]),
IntVector([x.hour for x in obj]),
IntVector([x.minute for x in obj]),
FloatSexpVector([x.second + x.microsecond * 1e-6 for x in obj])]
res = ISOdatetime(*d, tz=StrSexpVector([tzname]))
# TODO: can the POSIXct be created from the POSIXct constructor ?
# (is '<M8[ns]' mapping to Python datetime.datetime ?)
res = POSIXct(res)
elif obj.dtype.type == str:
res = _PANDASTYPE2RPY2[str](obj)
elif obj.dtype.name in integer_array_types:
res = _PANDASTYPE2RPY2[int](obj)
if len(obj.shape) == 1:
if obj.dtype != dt_O_type:
# force into an R vector
res = as_vector(res)
elif (obj.dtype == dt_O_type):
homogeneous_type = None
for x in obj.values:
if x is None:
continue
if homogeneous_type is None:
homogeneous_type = type(x)
continue
if ((type(x) is not homogeneous_type)
and not
((isinstance(x, float) and math.isnan(x))
or pandas.isna(x))):
raise ValueError(
'Series can only be of one type, or None '
'(and here we have %s and %s). If happening with '
'a pandas DataFrame the method infer_objects() '
'will normalize data types before conversion.' %
(homogeneous_type, type(x)))
# TODO: Could this be merged with obj.type.name == 'O' case above ?
res = _PANDASTYPE2RPY2[homogeneous_type](obj)
else:
# converted as a numpy array
func = numpy2ri.converter.py2rpy.registry[numpy.ndarray]
# current conversion as performed by numpy
res = func(obj.values)
if len(obj.shape) == 1:
if (obj.dtype != dt_O_type):
# force into an R vector
res = as_vector(res)
# "index" is equivalent to "names" in R
if obj.ndim == 1:
res.do_slot_assign('names',
StrVector(tuple(str(x) for x in obj.index)))
else:
res.do_slot_assign('dimnames',
SexpVector(conversion.py2rpy(obj.index)))
return res
